BitFunder Founder Charged by SEC for Stealing $61 Million Worth of BTC 1024

Today, the U.S. Securities and Exchange Commission (SEC) brought charges against Jon E. Montroll and his exchange BitFunder for operating an unregistered securities exchange and defrauding users. On top of that, the agency also charged the operator — Montroll — with making false and misleading statements in connection with an unregistered offering of securities. BitFunder was a platform that permitted users to buy and sell virtual “shares” of various digital currency-related enterprises in exchange for Bitcoin.

“We allege that BitFunder operated unlawfully as an unregistered securities exchange.  Platforms that engage in the activity of a national securities exchange, regardless of whether that activity involves digital assets, tokens, or coins, must register with the SEC or operate pursuant to an exemption.  We will continue to focus on these types of platforms to protect investors and ensure compliance with the securities laws,” said Marc Berger, Director of the SEC’s New York Regional Office.

The SEC’s complaint, filed in federal district court in Manhattan, charges Montroll and BitFunder with violations of the anti-fraud and registration provisions of the federal securities laws. The complaint seeks permanent injunctions and disgorgement plus interest and penalties. 

The agency alleges Montroll operated BitFunder as an unregistered online securities exchange and defrauded exchange users by misappropriating their Bitcoin, and also for failing to disclose a cyberattack on BitFunder’s system that resulted in the theft of more than 6,000 Bitcoin. Going off the price of Bitcoin today, that’s about $61,800,000.

“As alleged in the complaint, Montroll defrauded exchange users by misappropriating their bitcoins and failing to disclose a cyberattack on the exchange’s system and the resulting bitcoin theft.  We will continue to vigorously police conduct involving distributed ledger technology and ensure that bad actors who commit fraud in this space are held accountable,” said Lara S. Mehraban, Associate Regional Director of the SEC’s New York Regional Office.

Unfortunately for Montroll, his legal troubles don’t stop with the SEC: Also today, in a parallel criminal case, the U.S. Attorney’s Office for the Southern District of New York filed a complaint against him for perjury and obstruction of justice during the SEC’s investigation. This implies that Montroll, in some way, must have not fully cooperated with the SEC during the agency’s investigation.

This case comes as the SEC is cracking down on other companies and individuals it believes are partaking in shady business within the crypto-space. In January of this year, the agency advised people to “exercise caution” with Bitcoin and other digital currencies.

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Bedroom Trader OLI: Trade the World’s Crypto Markets Without Leaving Your Pillow 219

Why Establish an Office or Construct a Lavish Trading Desk?

Bedroom Trader OLI (OLI) demonstrates that participation in global markets requires nothing more than a smartphone, a Wi‑Fi connection, and a comfortable setting.

What Is OLI?

Bedroom Trader OLI represents a meme-driven cryptocurrency collective tailored for casual traders who prioritise convenience over traditional workspaces. The initiative is grounded in three foundational principles: unrestricted accessibility, decentralised community governance, and inclusive meme culture. Trading activities may commence at any time and location, free from conventional entry barriers. Token holders are empowered to direct major organisational decisions through decentralised autonomous organisation (DAO) voting mechanisms. A culture of humorous exchange and viral creativity encourages ease of entry and fosters a welcoming environment. OLI redefines participation in cryptocurrency by transforming it into a familiar daily activity that accommodates both newcomers and experienced market participants.

Tokenomics in Plain English

The OLI token supply dynamically adjusts in response to community activity. At the close of each month, a tally of active wallets is conducted, followed by the minting of 50 to 150 billion OLI tokens based on that figure. Fifty percent is allocated as a universal reward for all holders. Every wallet in possession of OLI at the time of the monthly snapshot receives an equal share, ensuring equity and preventing concentration of tokens among large holders. The remaining fifty percent is dedicated to ecosystem development. This allocation supports airdrops, marketing initiatives, strategic partnerships, liquidity provisioning, and the operational development fund. By linking token issuance to verified engagement, OLI cultivates a self-regulating economy that prioritises collective growth.

Roadmap Highlights

Q4 2024 – Launch of the Bedroom Trading Challenge, featuring the initial airdrop, social media competitions, and a beta leaderboard.
Q1 2025 – Implementation of DAO Voting Module, enabling live on-chain proposal submissions and governance procedures.
Q2 2025 – Release of OLI Swap, offering one-tap token swaps and liquidity farming directly via mobile platforms.
Q3 2025 – Initiation of Meme Collaboration Season, including partnerships with leading meme tokens to enhance reach and utility.

Join the Pillow‑Powered Revolution

The need for conventional office settings is obsolete. With the use of a mobile device and a comfortable environment, market participation becomes straightforward, interactive, and enjoyable.

Website – https://tradeoli.io

Trustee Plus Revolution: Hundreds of Visitors Instantly Received a Fraction of Bitcoin at Money20/20. How Did It Happen? 214

This year at Money20/20 in Amsterdam, the financial community witnessed a real sensation at the booth of the crypto wallet Trustee Plus. For the first time in the event’s history, anyone could receive a fraction of Bitcoin using only a mobile phone number — even if they had never used cryptocurrency before.

This innovative technology allows users to initiate a crypto transfer to a mobile number, even if the recipient is not yet a Trustee Plus user. Once the recipient downloads the app, the funds automatically appear in their balance. The received Bitcoin can then be held, exchanged, or spent.

This solution sparked significant interest among Money20/20 attendees. According to company representatives, nearly 500 visitors enriched their crypto portfolios thanks to the Trustee Plus booth.

“One of Trustee Plus’s core missions is to unlock the potential of future finance for everyday people. We believe cryptocurrency should work much more simply and intuitively than traditional banking services. And when we hear from new users, ‘I’ll remember this gifted piece of Bitcoin for the rest of my life’, it reminds us that everything we do truly matters,” said Vadym Hrusha, Founder of Trustee Plus.

Effortless Bitcoin Spending with Trustee

Trustee Plus also enables seamless conversion of Bitcoin to euros directly within the app. The converted funds can be used in several ways: via SEPA transfers to any European IBAN, or through the Quicko Digital virtual card, which is currently available for free issuance. All card operations are commission-free.

By combining an intuitive interface, instant transfers, and the ability to use crypto in everyday transactions, Trustee Plus takes another step toward the mass adoption of digital assets in Europe’s financial ecosystem.

Earlier, the largest crypto media outlet in Eastern Europe, Incrypted, included Trustee Plus in its list of the “Top 12 Best Cryptocurrency Projects for Paying with Bitcoin or Ethereum in 2025.”

Skywork-Reward-V2: Leading the New Milestone for Open-Source Reward Models 190

In September 2024, Skywork first open-sourced the Skywork-Reward series models and related datasets. Over the past nine months, these models and data have been widely adopted by the open-source community for research and practice, with over 750,000 cumulative downloads on the HuggingFace platform, helping multiple frontier models achieve excellent results in authoritative evaluations such as RewardBench.

On July 4, 2025, Skywork continues to open-source the second-generation reward models – the Skywork-Reward-V2 series, comprising 8 reward models based on different base models of varying sizes, with parameters ranging from 600 million to 8 billion. These models have achieved top rankings across seven major mainstream reward model evaluation benchmarks.

Skywork-Reward-V2 Download Links
HuggingFace: https://huggingface.co/collections/Skywork/skywork-reward-v2-685cc86ce5d9c9e4be500c84
GitHub: https://github.com/SkyworkAI/Skywork-Reward-V2
Technical Report: https://arxiv.org/abs/2507.01352

Reward models play a crucial role in the Reinforcement Learning from Human Feedback (RLHF) process. In developing this new generation of reward models, we constructed a hybrid dataset called Skywork-SynPref-40M, containing a total of 40 million preference pairs.

To achieve large-scale, efficient data screening and filtering, Skywork specially designed a two-stage human-machine collaborative process that combines high-quality human annotation with the scalable processing capabilities of models. In this process, humans provide rigorously verified high-quality annotations, while Large Language Models (LLMs) automatically organize and expand based on human guidance.

Based on the above high-quality hybrid preference data, we developed the Skywork-Reward-V2 series, which demonstrates broad applicability and excellent performance across multiple capability dimensions, including general alignment with human preferences, objective correctness, safety, resistance to style bias, and best-of-N scaling capability. Experimental validation shows that this series of models achieved the best performance on seven mainstream reward model evaluation benchmarks.

01 Skywork-SynPref-40M: Human-Machine Collaboration for Million-Scale Human Preference Data Screening

Even the most advanced current open-source reward models still perform inadequately on most mainstream evaluation benchmarks. They fail to effectively capture the subtle and complex characteristics of human preferences, particularly when facing multi-dimensional, multi-level feedback.

Additionally, many reward models tend to excel on specific benchmark tasks but struggle to transfer to new tasks or scenarios, exhibiting obvious “overfitting” phenomena. Although existing research has attempted to improve performance through optimizing objective functions, improving model architectures, and recently emerging Generative Reward Models, the overall effectiveness remains quite limited.

We believe that the current fragility of reward models mainly stems from the limitations of existing preference datasets, which often have limited coverage, mechanical label generation methods, or lack rigorous quality control.

Therefore, in developing the new generation of reward models, we not only continued the first generation’s experience in data optimization but also introduced more diverse and larger-scale real human preference data, striving to improve data scale while maintaining data quality.

Consequently, Skywork proposes Skywork-SynPref-40M – the largest preference hybrid dataset to date, containing a total of 40 million preference sample pairs. Its core innovation lies in a “human-machine collaboration, two-stage iteration” data selection pipeline.

Stage 1: Human-Guided Small-Scale High-Quality Preference Construction

The team first constructed an unverified initial preference pool and used Large Language Models (LLMs) to generate preference-related auxiliary attributes such as task type, objectivity, and controversy. Based on this, human annotators followed a strict verification protocol and used external tools and advanced LLMs to conduct detailed reviews of partial data, ultimately constructing a small-scale but high-quality “gold standard” dataset as the basis for subsequent data generation and model evaluation.

Subsequently, we used preference labels from the gold standard data as guidance, combined with LLM large-scale generation of high-quality “silver standard” data, thus achieving data volume expansion. The team also conducted multiple rounds of iterative optimization: in each round, training reward models and identifying model weaknesses based on their performance on gold standard data; then retrieving similar samples and using multi-model consensus mechanisms for automatic annotation to further expand and enhance silver standard data. This human-machine collaborative closed-loop process continues iteratively, effectively improving the reward model’s understanding and discrimination of preferences.

Stage 2: Fully Automated Large-Scale Preference Data Expansion

After obtaining preliminary high-quality models, the second stage turns to automated large-scale data expansion. This stage no longer relies on manual review but uses trained reward models to perform consistency filtering:

  • If a sample’s label is inconsistent with the current optimal model’s prediction, or if the model’s confidence is low, LLMs are called to automatically re-annotate;
  • If the sample label is consistent with the “gold model” (i.e., a model trained only on human data) prediction and receives support from the current model or LLM, it can directly pass screening.

Through this mechanism, the team successfully screened 26 million selected data points from the original 40 million samples, achieving a good balance between preference data scale and quality while greatly reducing the human annotation burden.

02 Skywork-Reward-V2: Matching Large Model Performance with Small Model Size

Compared to the previous generation Skywork-Reward, Skywork newly released Skywork-Reward-V2 series provides 8 reward models trained based on Qwen3 and LLaMA3 series models, with parameter scales covering from 600 million to 8 billion.

On seven mainstream reward model evaluation benchmarks including Reward Bench v1/v2, PPE Preference & Correctness, RMB, RM-Bench, and JudgeBench, the Skywork-Reward-V2 series comprehensively achieved current state-of-the-art (SOTA) levels.

Compensating for Model Scale Limitations with Data Quality and Richness

Even the smallest model, Skywork-Reward-V2-Qwen3-0.6B, achieves overall performance nearly matching the previous generation’s strongest model, Skywork-Reward-Gemma-2-27B-v0.2, on average. The largest scale model, Skywork-Reward-V2-Llama-3.1-8B, achieved comprehensive superiority across all mainstream benchmark tests, becoming the currently best-performing open-source reward model overall.

Broad Coverage of Multi-Dimensional Human Preference Capabilities

Additionally, Skywork-Reward-V2 achieved leading results in multiple advanced capability evaluations, including Best-of-N (BoN) tasks, bias resistance capability testing (RM-Bench), complex instruction understanding, and truthfulness judgment (RewardBench v2), demonstrating excellent generalization ability and practicality.

Highly Scalable Data Screening Process Significantly Improves Reward Model Performance

Beyond excellent performance in evaluations, the team also found that in the “human-machine collaboration, two-stage iteration” data construction process, preference data that underwent careful screening and filtering could continuously and effectively improve reward models’ overall performance through multiple iterative training rounds, especially showing remarkable performance in the second stage’s fully automated data expansion.

In contrast, blindly expanding raw data not only fails to improve initial performance but may introduce noise and negative effects. To further validate the critical role of data quality, we conducted experiments on a subset of 16 million data points from an early version. Results showed that training an 8B-scale model using only 1.8% (about 290,000) of the high-quality data already exceeded the performance of current 70B-level SOTA reward models. This result again confirms that the Skywork-SynPref dataset not only leads in scale but also has significant advantages in data quality.

03 Welcoming a New Milestone for Open-Source Reward Models: Helping Build Future AI Infrastructure

In this research work on the second-generation reward model Skywork-Reward-V2, the team proposed Skywork-SynPref-40M, a hybrid dataset containing 40 million preference pairs (with 26 million carefully screened pairs), and Skywork-Reward-V2, a series of eight reward models with state-of-the-art performance designed for broad task applicability.

We believe this research work and the continued iteration of reward models will help advance the development of open-source reward models and more broadly promote progress in Reinforcement Learning from Human Feedback (RLHF) research. This represents an important step forward for the field and can further accelerate the prosperity of the open-source community.

The Skywork-Reward-V2 series models focus on research into scaling preference data. In the future, the team’s research scope will gradually expand to other areas that have not been fully explored, such as alternative training techniques and modeling objectives.

Meanwhile, considering recent development trends in the field – reward models and reward shaping mechanisms have become core components in today’s large-scale language model training pipelines, applicable not only to RLHF based on human preference learning and behavior guidance, but also to RLVR including mathematics, programming, or general reasoning tasks, as well as agent-based learning scenarios.

Therefore, we envision that reward models, or more broadly, unified reward systems, are poised to form the core of AI infrastructure in the future. They will no longer merely serve as evaluators of behavior or correctness, but will become the “compass” for intelligent systems navigating complex environments, helping them align with human values and continuously evolve toward more meaningful goals.

Additionally, Skywork released the world’s first deep research AI workspace agents in May, which you can experience by visiting: skywork.ai

STONEFORM Launches a Tokenized Real Estate Platform to Open Up Investment Opportunities 158

Tokenizing real estate to Unveil global opportunities & fractional ownership for all investors.

STONEFORM is reshaping the real estate investment landscape by leveraging blockchain technology to create a decentralized platform for fractional property ownership, expanding global access and liquidity for investors. Through the power of tokenization, STONEFORM is set to make property ownership more accessible, efficient, and transparent by allowing fractional ownership of real estate assets.

STONEFORM’s Vision: A New Digital Paradigm for Real Estate Investment

STONEFORM’s goal is to integrate blockchain technology and decentralized finance (DeFi) to unveil the power of real estate investment. STONEFORM enables global participation, providing diverse investment options for individuals and institutions. Token holders can engage in real estate investments without the burdens typically associated with traditional property ownership.

“At STONEFORM,we are building more than just a platform; we are building a milestone in real estate, We believe blockchain is the key to facilitating widespread access to high-quality real estate assets, enabling anyone, regardless of their financial background, to invest in and benefit from the growth of this sector.” Ukrit Thaweerat, Founder.

Main Functionalities of STONEFORM

  • Fractional Ownership: Purchase fractional shares of premium real estate, lowering entry barriers for small investors globally.
  • Blockchain-Powered Liquidity: Tokenized assets trade on decentralized markets,ensuring faster and more cost-effective transactions.
  • Smart Contracts for Automated Management: Automates property management tasks like rent distribution, reducing costs and administrative efforts.
  • Decentralized Governance: Token holders vote on decisions,giving the community control over the platform’s governance and direction.
  • Global Access: Blockchain enables worldwide participation in real estate investment.
  • Security and Compliance: Robust security features and automated compliance checks ensure safe and regulated transactions.

A New Era for Real Estate Investment

The global real estate market is valued at trillions, but traditional investments often require large capital and have limited liquidity. STONEFORM solves these issues with blockchain-powered fractional ownership.

Conclusion

STONEFORM is redefining the way people invest in real estate by integrating blockchain and decentralized finance. With fractional ownership, smart contracts, and decentralized governance, the platform is set to make real estate investment more accessible,liquid,and transparent than ever before. The project will continue to expand its offerings, driving the future of real estate investment on a global scale.

Zycus and Akool Disrupt the C-Suite: When AI Challenges the Irreplaceable 310

While the industry obsesses over AI replacing junior analysts and mid-level managers, Akool and Zycus just shattered the ultimate glass ceiling: they’ve digitally cloned a CEO. At Horizon SEA 2025 in Malaysia, these companies unleashed an AI avatar of Zycus Founder and CEO Aatish Dedhia that engaged senior procurement leaders in real-time strategic dialogue about competitive positioning and transformation.

This isn’t incremental innovation. This is a direct assault on the belief that visionary leadership can’t be codified.

Most GenAI applications target the bottom of the org chart—automating reports, scheduling meetings, drafting emails. But this partnership proved that even the most sacred executive function—strategic vision and thought leadership—can begin to be digitized, scaled, and deployed at will.

“This partnership represents the convergence of cutting-edge AI avatar technology with deep industry expertise,” said Jiajun (Jeff) Lu, Founder & CEO at Akool. “The avatar was trained on over 20–30 minutes of personalized video content, enriched with proprietary procurement knowledge and competitive intelligence. The result was a highly interactive digital spokesperson capable of delivering strategic insights at scale.”

“Everyone’s been playing it safe, automating the obvious,” said Amit Shah, CMO & Global Head BD at Zycus. “We’ve taken the first bold step—creating a digital CEO that could hold meaningful conversations with C-suite executives across APAC. This is just the beginning, and naturally the avatar has a long way to go, but it proves that audacity opens doors others won’t even attempt.”

This breakthrough stems from Dedhia’s two-decade journey as an AI pioneer. Long before “AI” became boardroom buzzword bingo, he was building Merlin AI—proving his conviction that AI could fundamentally transform enterprise operations. Today, it’s Dedhia who pushes his team beyond conventional thinking, demanding they abandon the obvious for the seemingly impossible. True innovation requires audacity over comfort zones.

The real disruption isn’t in the current technology—it’s in the precedent being set. If the initial replication of strategic insights from a founder and CEO shows this much promise, what executive function might eventually be scalable?

Both companies are now exploring how to evolve this breakthrough across client experiences and training programs.

The message is clear: in a world where even the first attempts at digitizing founders show promise, the only competitive advantage is the courage to challenge what everyone else believes is impossible.

About Akool

Akool is a complete AI Video Generation Suite, transforming how professional video content is created. From real-time digital avatars to lip sync, face swap, and multilingual video translation, Akool empowers individuals and enterprises to produce studio-quality videos at scale—without actors, cameras, or production teams. Learn more at www.akool.com.

About Zycus

Zycus is a recognized market leader of AI powered procurement solutions and pioneer of the world’s first Source-to-Pay platform with Agentic AI framework and Intake Management. Trusted by leading global enterprises, Zycus leverages advanced automation and analytics to drive intelligent transformation and push the boundaries of innovation. Visit www.zycus.com for more.

Solidus AI Tech and Secret Network Partner to deploy Confidential AI Tools 450

Solidus AI Tech and Secret Network have begun a technical and strategic collaboration to bring confidential computing capabilities to Solidus AI Tech’s decentralized AI platform. The partnership focuses on enabling developers to build and deploy privacy-preserving AI models and tools using Secret Network’s secure infrastructure.

As a first step, AI Tech has integrated Secret Network’s privacy-preserving large language model, SecretAI – DeepSeek R1, into Solidus AI Tech’s foundation AI model library. This model is designed to support confidential inference, ensuring that user inputs remain inaccessible to infrastructure providers and model hosts. Solidus AI Tech has agreed to evaluate the technical integration with its development team.

The two teams are currently exploring the integration of SecretVM into the Solidus AI Tech platform as a dedicated AI app. This would enable developers to deploy agents in confidential and verifiable environments, leveraging Trusted Execution Environments (TEEs) to protect sensitive data throughout the lifecycle of an AI application and utilize cryptographic verifiability to the code any agent runs.

“This collaboration reflects a shared commitment to building secure and decentralized AI infrastructure,” said John Mendez of Solidus AI Tech. “Confidential computing is an important layer for developers and users alike, and we’re excited to work with Secret Network to provide the tools people need.”

Lisa Loud, Executive Director of the Secret Network Foundation, added, “We see this collaboration as an important step in advancing privacy and trust in AI. By working with Solidus AI Tech, we’re making confidential computing more accessible to developers who are building the next generation of intelligent systems.”

Further discussions are underway to explore a confidential deployment option within Solidus AI Tech’s Agent Forge interface. This feature would allow users to select SecretVM as their execution environment during agent creation. Both teams are also aligned on creating a knowledge-based agent trained on Secret Network documentation to assist developers, and have committed to joint marketing and outreach as the integration progresses.

The partnership builds on a shared vision of ethical AI deployment, data protection, and transparent infrastructure. The teams have agreed to continue development planning, sandbox testing, and follow-up meetings in the coming weeks.

About Solidus AI Tech

Solidus AI Tech is an AI infrastructure provider offering GPU-based compute services and application hosting. Its platform includes an AI agent marketplace, model deployment tools, and tokenized access to AI resources for developers across both Web3 and traditional industries. Learn more at https://ai.aitech.io.

About Secret Network

Secret Network is the first blockchain with native support for confidential computation. Built on Trusted Execution Environments (TEEs), it enables private and verifiable execution of smart contracts and AI models. SecretVM and SecretAI provide the infrastructure for developers to build secure and trusted AI systems. Learn more at https://secretai.scrtlabs.com.